#!/usr/bin/env python3
"""
Post-hoc Analysis: cos(original, swapped) per sample

Loads saved vectors_{scale}.npz from exp2a_swap_analysis results,
computes cosine similarity between original and swapped embeddings per sample,
and reports category-level statistics.

This measures whether the model's representation actually changes when
obj1↔obj2 are swapped — the fundamental test of spatial relation encoding.

Usage:
    python compute_swap_cosine.py --model_type molmo
    python compute_swap_cosine.py --model_type molmo --results_dir /path/to/results
"""

import os
import json
import argparse
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

CATEGORY_ORDER = ['left', 'right', 'above', 'under', 'far', 'close']
GROUP_MAP = {
    'left': 'horizontal', 'right': 'horizontal',
    'above': 'vertical', 'under': 'vertical',
    'far': 'distance', 'close': 'distance',
}
GROUP_ORDER = ['horizontal', 'vertical', 'distance']

SCALE_COLORS = {
    'vanilla': '#1f77b4', '80k': '#ff7f0e', '400k': '#2ca02c',
    '800k': '#d62728', '2m': '#9467bd', 'roborefer': '#8c564b',
}


def cosine_sim_per_sample(orig: np.ndarray, swap: np.ndarray) -> np.ndarray:
    """Compute cosine similarity per row (sample)."""
    # orig, swap: (N, D)
    dot = np.sum(orig * swap, axis=1)
    norm_o = np.linalg.norm(orig, axis=1)
    norm_s = np.linalg.norm(swap, axis=1)
    return dot / (norm_o * norm_s + 1e-10)


def analyze_scale(npz_path: str, scale: str) -> dict:
    """Analyze one scale's NPZ file. Returns dict of results."""
    data = np.load(npz_path, allow_pickle=True)

    # Find available layers
    layer_keys = sorted([k for k in data.files if k.startswith('orig_L')])
    layers = [int(k.replace('orig_L', '')) for k in layer_keys]

    scale_results = {}

    for layer in layers:
        orig = data.get(f'orig_L{layer}')
        swap = data.get(f'swap_L{layer}')
        labels = data.get(f'labels_L{layer}')

        if orig is None or swap is None or labels is None:
            continue

        labels = np.array([str(l) for l in labels])
        cos_sims = cosine_sim_per_sample(orig, swap)

        layer_result = {
            'overall_mean': float(np.mean(cos_sims)),
            'overall_std': float(np.std(cos_sims)),
            'overall_n': len(cos_sims),
        }

        # Per category
        for cat in CATEGORY_ORDER:
            mask = labels == cat
            if mask.any():
                cat_sims = cos_sims[mask]
                layer_result[f'{cat}_mean'] = float(np.mean(cat_sims))
                layer_result[f'{cat}_std'] = float(np.std(cat_sims))
                layer_result[f'{cat}_n'] = int(mask.sum())

        # Per group
        for group in GROUP_ORDER:
            group_cats = [c for c in CATEGORY_ORDER if GROUP_MAP[c] == group]
            mask = np.isin(labels, group_cats)
            if mask.any():
                group_sims = cos_sims[mask]
                layer_result[f'{group}_mean'] = float(np.mean(group_sims))
                layer_result[f'{group}_std'] = float(np.std(group_sims))

        scale_results[layer] = layer_result

    return scale_results


def plot_swap_cosine_by_layer(
    all_results: dict,  # {scale: {layer: {category_mean, ...}}}
    model_type: str,
    save_path: str,
):
    """Plot cos(orig, swap) across layers for each scale."""
    fig, axes = plt.subplots(1, 3, figsize=(21, 6))
    scale_order = ['vanilla', '80k', '400k', '800k', '2m', 'roborefer']

    for idx, group in enumerate(GROUP_ORDER):
        ax = axes[idx]
        for scale in scale_order:
            if scale not in all_results:
                continue
            results = all_results[scale]
            layers = sorted(results.keys())
            vals = [results[l].get(f'{group}_mean', np.nan) for l in layers]
            color = SCALE_COLORS.get(scale, 'gray')
            ax.plot(layers, vals, '-o', color=color, label=scale, linewidth=2, markersize=5)

        ax.set_xlabel('Layer Index', fontsize=11)
        ax.set_ylabel('cos(original, swapped)', fontsize=11)
        ax.set_title(f'{group}', fontsize=13, fontweight='bold')
        ax.legend(fontsize=9)
        ax.grid(True, alpha=0.3)
        ax.set_ylim(None, 1.02)

    fig.suptitle(
        f'{model_type.upper()} - cos(original, swapped) Across Layers\n'
        f'(Lower = model distinguishes swap more)',
        fontsize=14, fontweight='bold', y=1.04
    )
    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Saved: {save_path}")


def plot_swap_cosine_barplot(
    all_results: dict,
    model_type: str,
    save_path: str,
):
    """Bar plot at deepest layer: per-category cos(orig, swap) across scales."""
    scale_order = ['vanilla', '80k', '400k', '800k', '2m', 'roborefer']
    available_scales = [s for s in scale_order if s in all_results]

    if not available_scales:
        return

    # Use deepest layer
    sample_layers = sorted(all_results[available_scales[0]].keys())
    deepest = sample_layers[-1]

    fig, ax = plt.subplots(figsize=(14, 6))
    x = np.arange(len(CATEGORY_ORDER))
    width = 0.8 / len(available_scales)

    for i, scale in enumerate(available_scales):
        results = all_results[scale]
        if deepest not in results:
            continue
        layer_data = results[deepest]
        vals = [layer_data.get(f'{cat}_mean', 0) for cat in CATEGORY_ORDER]
        offset = (i - len(available_scales) / 2 + 0.5) * width
        color = SCALE_COLORS.get(scale, 'gray')
        bars = ax.bar(x + offset, vals, width, label=scale, color=color)
        for bar, val in zip(bars, vals):
            if val > 0:
                ax.annotate(f'{val:.3f}', xy=(bar.get_x() + bar.get_width() / 2, bar.get_height()),
                            xytext=(0, 2), textcoords='offset points',
                            ha='center', va='bottom', fontsize=6, rotation=90)

    ax.set_xticks(x)
    ax.set_xticklabels(CATEGORY_ORDER, fontsize=11)
    ax.set_ylabel('cos(original, swapped)', fontsize=12)
    ax.set_title(f'{model_type.upper()} - Layer {deepest}: cos(original, swapped) by Category\n'
                 f'(Lower = model representation changes more on swap)',
                 fontsize=13, fontweight='bold')
    ax.legend(fontsize=9)
    ax.grid(True, alpha=0.3, axis='y')

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Saved: {save_path}")


def plot_swap_cosine_distribution(
    all_results_raw: dict,  # {scale: {layer: {'sims': array, 'labels': array}}}
    model_type: str,
    save_dir: str,
):
    """Histogram of per-sample cos(orig, swap) at deepest layer, per group."""
    scale_order = ['vanilla', '80k', '400k', '800k', '2m', 'roborefer']
    available_scales = [s for s in scale_order if s in all_results_raw]

    if not available_scales:
        return

    sample_layers = sorted(all_results_raw[available_scales[0]].keys())
    deepest = sample_layers[-1]

    for group in GROUP_ORDER:
        fig, axes = plt.subplots(1, len(available_scales), figsize=(5 * len(available_scales), 4),
                                  sharey=True, sharex=True)
        if len(available_scales) == 1:
            axes = [axes]

        for i, scale in enumerate(available_scales):
            ax = axes[i]
            raw = all_results_raw[scale].get(deepest)
            if raw is None:
                continue
            sims = raw['sims']
            labels = raw['labels']
            group_cats = [c for c in CATEGORY_ORDER if GROUP_MAP[c] == group]
            mask = np.isin(labels, group_cats)

            if mask.any():
                group_sims = sims[mask]
                ax.hist(group_sims, bins=30, alpha=0.7, color=SCALE_COLORS.get(scale, 'gray'),
                        edgecolor='white', linewidth=0.5)
                ax.axvline(np.mean(group_sims), color='red', linestyle='--', linewidth=1.5,
                           label=f'mean={np.mean(group_sims):.3f}')
                ax.legend(fontsize=8)

            ax.set_title(f'{scale}', fontsize=11, fontweight='bold')
            ax.set_xlabel('cos(orig, swap)', fontsize=9)
            if i == 0:
                ax.set_ylabel('Count', fontsize=9)

        fig.suptitle(f'{model_type.upper()} - {group} - Layer {deepest}: Distribution of cos(orig, swap)',
                     fontsize=13, fontweight='bold')
        plt.tight_layout()
        plt.savefig(os.path.join(save_dir, f'swap_cosine_dist_{group}.png'), dpi=200, bbox_inches='tight')
        plt.close()

    print(f"Saved distribution plots to {save_dir}")


def main():
    parser = argparse.ArgumentParser(description='Post-hoc: cos(original, swapped) analysis')
    parser.add_argument('--model_type', type=str, required=True, choices=['molmo', 'nvila', 'qwen'])
    parser.add_argument('--results_dir', type=str,
                        default='/data/shared/Qwen/experiments/exp2a_swap_analysis/results')
    args = parser.parse_args()

    model_dir = os.path.join(args.results_dir, args.model_type)
    plots_dir = os.path.join(model_dir, 'plots')
    os.makedirs(plots_dir, exist_ok=True)

    scale_order = ['vanilla', '80k', '400k', '800k', '2m', 'roborefer']

    all_results = {}       # {scale: {layer: stats_dict}}
    all_results_raw = {}   # {scale: {layer: {'sims': array, 'labels': array}}}

    for scale in scale_order:
        npz_path = os.path.join(model_dir, f'vectors_{scale}.npz')
        if not os.path.exists(npz_path):
            continue

        print(f"\nProcessing {args.model_type} - {scale}")
        results = analyze_scale(npz_path, scale)
        all_results[scale] = results

        # Also extract raw per-sample cosines for distribution plots
        data = np.load(npz_path, allow_pickle=True)
        raw_layers = {}
        for layer in sorted(results.keys()):
            orig = data.get(f'orig_L{layer}')
            swap = data.get(f'swap_L{layer}')
            labels = data.get(f'labels_L{layer}')
            if orig is not None and swap is not None and labels is not None:
                sims = cosine_sim_per_sample(orig, swap)
                raw_layers[layer] = {
                    'sims': sims,
                    'labels': np.array([str(l) for l in labels]),
                }
        all_results_raw[scale] = raw_layers

        # Print summary for deepest layer
        deepest = sorted(results.keys())[-1]
        r = results[deepest]
        print(f"  Layer {deepest} (deepest):")
        print(f"    Overall: {r['overall_mean']:.4f} ± {r['overall_std']:.4f} (n={r['overall_n']})")
        for cat in CATEGORY_ORDER:
            m = r.get(f'{cat}_mean')
            s = r.get(f'{cat}_std')
            n = r.get(f'{cat}_n', 0)
            if m is not None:
                print(f"    {cat:>6s}: {m:.4f} ± {s:.4f} (n={n})")

    if not all_results:
        print("No data found. Check results_dir.")
        return

    # Save JSON
    json_data = {}
    for scale, layers in all_results.items():
        json_data[scale] = {str(l): v for l, v in layers.items()}
    json_path = os.path.join(model_dir, 'swap_cosine_stats.json')
    with open(json_path, 'w') as f:
        json.dump(json_data, f, indent=2)
    print(f"\nSaved stats: {json_path}")

    # Save CSV (one row per scale×layer)
    csv_rows = []
    for scale, layers in all_results.items():
        for layer, stats in sorted(layers.items()):
            row = {'scale': scale, 'layer': layer}
            row.update(stats)
            csv_rows.append(row)
    csv_path = os.path.join(model_dir, 'swap_cosine_stats.csv')
    pd.DataFrame(csv_rows).to_csv(csv_path, index=False)
    print(f"Saved CSV: {csv_path}")

    # Plots
    plot_swap_cosine_by_layer(all_results, args.model_type,
                               os.path.join(plots_dir, 'swap_cosine_by_layer.png'))
    plot_swap_cosine_barplot(all_results, args.model_type,
                             os.path.join(plots_dir, 'swap_cosine_barplot.png'))
    plot_swap_cosine_distribution(all_results_raw, args.model_type, plots_dir)

    print(f"\nDone. Results in: {model_dir}")


if __name__ == '__main__':
    main()